ANN_Force | ANN force plugin for OpenMM with native CUDA support
kandi X-RAY | ANN_Force Summary
kandi X-RAY | ANN_Force Summary
ANN_Force is a C++ library. ANN_Force has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.
This is the ANN_Force biasing force plugin for OpenMM (and accelerated sampling with data-augmented autoencoders framework (
This is the ANN_Force biasing force plugin for OpenMM (and accelerated sampling with data-augmented autoencoders framework (
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Quality
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Support
ANN_Force has a low active ecosystem.
It has 5 star(s) with 1 fork(s). There are 1 watchers for this library.
It had no major release in the last 6 months.
There are 0 open issues and 1 have been closed. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of ANN_Force is current.
Quality
ANN_Force has no bugs reported.
Security
ANN_Force has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
ANN_Force is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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ANN_Force releases are not available. You will need to build from source code and install.
Installation instructions, examples and code snippets are available.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of ANN_Force
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of ANN_Force
ANN_Force Key Features
No Key Features are available at this moment for ANN_Force.
ANN_Force Examples and Code Snippets
No Code Snippets are available at this moment for ANN_Force.
Community Discussions
No Community Discussions are available at this moment for ANN_Force.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install ANN_Force
Modify CMake installation settings as needed. Turn on CUDA option if you would like to run simulation on CUDA. Then run. Root permission may be needed.
This package should be used together with OpenMM simulation package and training results of neural networks. A good starting point would be tests in this framework: https://github.com/weiHelloWorld/accelerated_sampling_with_autoencoder.
This package should be used together with OpenMM simulation package and training results of neural networks. A good starting point would be tests in this framework: https://github.com/weiHelloWorld/accelerated_sampling_with_autoencoder.
Support
For any questions, feel free to contact weichen9@illinois.edu or open a github issue.
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